Leland Heyman, Lead Data Scientist at Sherlock Biosciences -
Feb 14, 2024 0:45:23 GMT -5
Post by sabbirislam258 on Feb 14, 2024 0:45:23 GMT -5
Leland Hyman is the Lead Data Scientist. Sherlock Biosciences . He is an experienced computer scientist and researcher with a background in Machine learning and molecular diagnostics. Sherlock Biosciences is a biotechnology company based in Cambridge, Massachusetts that is developing diagnostic tests using CRISPR. They aim to disrupt molecular diagnostics with better, faster, cheaper tests. What initially drew you to computer science? I started programming at a very young age, but I was mainly interested in making video games with my friends. During college and graduate school my interest in other applications of computer science grew, especially with all the important machine learning work happening in the early 2010s. The whole field seemed like an exciting new frontier that could directly affect scientific research and our daily lives—I couldn't help but be impressed.
You also did your Ph.D. In cellular and molecular biology, when did you first realize Hungary Telemarketing Data that the two fields would intersect? I started doing this kind of intersectional work with computer science and biology early on in graduate school. My lab focused on solving protein engineering problems through collaboration between diehard biochemists, computer scientists, and everyone in between. I quickly recognized that machine learning could provide valuable insights into biological systems and greatly simplify experiments. Conversely, I also gained an appreciation for the value of biological intuition when building machine learning models. In my view, formulating the problem correctly is the key factor in machine learning. This is why I believe that joint efforts in different fields can have a profound impact.
Since 2022 you have been working at Sherlock Biosciences, can you give some details about what your role entails? I currently lead the computational team at Sherlock Biosciences. Our group is responsible for designing the components that go into our diagnostic assays, interacting with experimenters who test these designs in the wet lab, and developing new computational capabilities to optimize the designs. . In addition to coordinating these activities, I work on the machine learning parts of our codebase, experimenting with new model architectures and new ways to simulate DNA and RNA physics. Machine learning is a core part of Sherlock Biosciences, can you explain the type of data and the volume of data that is being collected, and then how ML parses that data? During assay development, we test dozens to hundreds of candidates for each new pathogen.
You also did your Ph.D. In cellular and molecular biology, when did you first realize Hungary Telemarketing Data that the two fields would intersect? I started doing this kind of intersectional work with computer science and biology early on in graduate school. My lab focused on solving protein engineering problems through collaboration between diehard biochemists, computer scientists, and everyone in between. I quickly recognized that machine learning could provide valuable insights into biological systems and greatly simplify experiments. Conversely, I also gained an appreciation for the value of biological intuition when building machine learning models. In my view, formulating the problem correctly is the key factor in machine learning. This is why I believe that joint efforts in different fields can have a profound impact.
Since 2022 you have been working at Sherlock Biosciences, can you give some details about what your role entails? I currently lead the computational team at Sherlock Biosciences. Our group is responsible for designing the components that go into our diagnostic assays, interacting with experimenters who test these designs in the wet lab, and developing new computational capabilities to optimize the designs. . In addition to coordinating these activities, I work on the machine learning parts of our codebase, experimenting with new model architectures and new ways to simulate DNA and RNA physics. Machine learning is a core part of Sherlock Biosciences, can you explain the type of data and the volume of data that is being collected, and then how ML parses that data? During assay development, we test dozens to hundreds of candidates for each new pathogen.